class_docstr: :class:`~opacus.optimizers.optimizer.DPOptimizer` that implements
  per layer clipping strategy
code: def clip_and_accumulate(self):
      for p, max_grad_norm in zip(self.params, self.max_grad_norms):
          _check_processed_flag(p.grad_sample)
  
          grad_sample = self._get_flat_grad_sample(p)
          per_sample_norms = grad_sample.norm(
              2, dim=tuple(range(1, grad_sample.ndim))
          )
          per_sample_clip_factor = (
              max_grad_norm / (per_sample_norms + 1e-6)
          ).clamp(max=1.0)
          grad = contract("i,i...", per_sample_clip_factor, grad_sample)
  
          if p.summed_grad is not None:
              p.summed_grad += grad
          else:
              p.summed_grad = grad
  
          _mark_as_processed(p.grad_sample)
  
file_path: opacus/opacus/optimizers/perlayeroptimizer.py
imports: from __future__ import annotations
  from typing import List, Optional
  import torch
  from opacus.optimizers.utils import params
  from opt_einsum import contract
  from torch.optim import Optimizer
  from .optimizer import DPOptimizer, _check_processed_flag, _mark_as_processed
label_desc: Use image node_id 2 for calling the DPPerLayerOptimizer obj's underlying member method code with example usage: obj.clip_and_accumulate() without return types